



"""simple docstring"""

from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_						:    str				=      {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}
class     lowerCAmelCase__						(	_UpperCAmelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= '''ernie_m'''
	_SCREAMING_SNAKE_CASE	:		str  						= {'''dropout''': '''classifier_dropout''', '''num_classes''': '''num_labels'''}





	def __init__(      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[Any] = 250_002     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any] = 768     ,       _SCREAMING_SNAKE_CASE				:       Any = 12     ,       _SCREAMING_SNAKE_CASE				:       Tuple = 12     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any] = 3_072     ,       _SCREAMING_SNAKE_CASE				:       Dict = "gelu"     ,       _SCREAMING_SNAKE_CASE				:       List[Any] = 0.1     ,       _SCREAMING_SNAKE_CASE				:       Tuple = 0.1     ,       _SCREAMING_SNAKE_CASE				:       Dict = 514     ,       _SCREAMING_SNAKE_CASE				:       Any = 0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any] = 1     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any] = 1E-0_5     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=False     ,       _SCREAMING_SNAKE_CASE				:       int=0.0     ,       **_SCREAMING_SNAKE_CASE				:       int     ,       )						->	Tuple:





					"""simple docstring"""


					super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     vocab_size
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							int							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							int							=     hidden_act
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							int							=     initializer_range
					SCREAMING_SNAKE_CASE						:							List[Any]							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     classifier_dropout
					SCREAMING_SNAKE_CASE						:							List[str]							=     is_decoder
					SCREAMING_SNAKE_CASE						:							str							=     act_dropout












"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






